| Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 919 | 2024 |
| Visual reinforcement learning with imagined goals AV Nair, V Pong, M Dalal, S Bahl, S Lin, S Levine Advances in neural information processing systems 31, 2018 | 722 | 2018 |
| Residual reinforcement learning for robot control T Johannink, S Bahl, A Nair, J Luo, A Kumar, M Loskyll, JA Ojea, ... 2019 international conference on robotics and automation (ICRA), 6023-6029, 2019 | 658 | 2019 |
| Skew-fit: State-covering self-supervised reinforcement learning VH Pong, M Dalal, S Lin, A Nair, S Bahl, S Levine arXiv preprint arXiv:1903.03698, 2019 | 352 | 2019 |
| Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards G Schoettler, A Nair, J Luo, S Bahl, JA Ojea, E Solowjow, S Levine arXiv preprint arXiv:1906.05841, 2019 | 273 | 2019 |
| Affordances from human videos as a versatile representation for robotics S Bahl, R Mendonca, L Chen, U Jain, D Pathak Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 268 | 2023 |
| Human-to-robot imitation in the wild S Bahl, A Gupta, D Pathak arXiv preprint arXiv:2207.09450, 2022 | 221 | 2022 |
| Videodex: Learning dexterity from internet videos K Shaw, S Bahl, D Pathak Conference on Robot Learning, 654-665, 2023 | 160 | 2023 |
| Structured world models from human videos R Mendonca, S Bahl, D Pathak arXiv preprint arXiv:2308.10901, 2023 | 144 | 2023 |
| Neural dynamic policies for end-to-end sensorimotor learning S Bahl, M Mukadam, A Gupta, D Pathak Advances in Neural Information Processing Systems 33, 5058-5069, 2020 | 109 | 2020 |
| Open x-embodiment: Robotic learning datasets and rt-x models Q Vuong, S Levine, HR Walke, K Pertsch, A Singh, R Doshi, C Xu, J Luo, ... Towards Generalist Robots: Learning Paradigms for Scalable Skill Acquisition …, 2023 | 105 | 2023 |
| Contextual imagined goals for self-supervised robotic learning A Nair, S Bahl, A Khazatsky, V Pong, G Berseth, S Levine Conference on Robot Learning, 530-539, 2020 | 104 | 2020 |
| Playfusion: Skill acquisition via diffusion from language-annotated play L Chen, S Bahl, D Pathak Conference on Robot Learning, 2012-2029, 2023 | 69 | 2023 |
| Hrp: Human affordances for robotic pre-training MK Srirama, S Dasari, S Bahl, A Gupta arXiv preprint arXiv:2407.18911, 2024 | 39 | 2024 |
| Deft: Dexterous fine-tuning for real-world hand policies A Kannan, K Shaw, S Bahl, P Mannam, D Pathak arXiv preprint arXiv:2310.19797, 2023 | 35 | 2023 |
| Hierarchical neural dynamic policies S Bahl, A Gupta, D Pathak arXiv preprint arXiv:2107.05627, 2021 | 34 | 2021 |
| Rb2: Robotic manipulation benchmarking with a twist S Dasari, J Wang, J Hong, S Bahl, Y Lin, A Wang, A Thankaraj, K Chahal, ... arXiv preprint arXiv:2203.08098, 2022 | 31 | 2022 |
| Alan: Autonomously exploring robotic agents in the real world R Mendonca, S Bahl, D Pathak arXiv preprint arXiv:2302.06604, 2023 | 27 | 2023 |
| Learning dexterity from human hand motion in internet videos K Shaw, S Bahl, A Sivakumar, A Kannan, D Pathak The International Journal of Robotics Research 43 (4), 513-532, 2024 | 22 | 2024 |
| Efficient rl via disentangled environment and agent representations K Gmelin, S Bahl, R Mendonca, D Pathak arXiv preprint arXiv:2309.02435, 2023 | 15 | 2023 |
Deepak PathakCarnegie Mellon University | Skild AIVerified email at cs.cmu.edu
